Overview

Dataset statistics

Number of variables27
Number of observations46482
Missing cells65981
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.1 MiB
Average record size in memory1.1 KiB

Variable types

CAT14
NUM12
UNSUPPORTED1

Warnings

Complaint Number has a high cardinality: 40587 distinct values High cardinality
Address has a high cardinality: 14077 distinct values High cardinality
Received Date has a high cardinality: 4447 distinct values High cardinality
Entry Date has a high cardinality: 4190 distinct values High cardinality
Disposition Date has a high cardinality: 4008 distinct values High cardinality
Location has a high cardinality: 13667 distinct values High cardinality
Inspection Number is highly correlated with 311 Case NumberHigh correlation
311 Case Number is highly correlated with Inspection NumberHigh correlation
Neighborhoods - Analysis Boundaries is highly correlated with Neighborhoods_from_fyvs_ahh9 and 1 other fieldsHigh correlation
Neighborhoods_from_fyvs_ahh9 is highly correlated with Neighborhoods - Analysis Boundaries and 1 other fieldsHigh correlation
Neighborhoods (old) is highly correlated with Neighborhoods_from_fyvs_ahh9 and 1 other fieldsHigh correlation
Fire Prevention District is highly correlated with BattalionHigh correlation
Battalion is highly correlated with Fire Prevention DistrictHigh correlation
Primary has 5908 (12.7%) missing values Missing
311 Case Number has 45354 (97.6%) missing values Missing
Inspection Number has 7999 (17.2%) missing values Missing
Station Area has 482 (1.0%) missing values Missing
Disposition has 2010 (4.3%) missing values Missing
Disposition Date has 2495 (5.4%) missing values Missing
Fire Prevention Districts has 520 (1.1%) missing values Missing
Complaint Number is uniformly distributed Uniform
Complaint Id has unique values Unique
Complaint Item Type is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2020-12-13 01:58:56.732655
Analysis finished2020-12-13 01:59:16.424601
Duration19.69 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Complaint Id
Categorical

UNIQUE

Distinct46482
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size363.3 KiB
0704-011199Y
 
1
0912-017005Y
 
1
1804-009598Y
 
1
1210-012105Y
 
1
1807-017506
 
1
Other values (46477)
46477 
ValueCountFrequency (%) 
0704-011199Y1< 0.1%
 
0912-017005Y1< 0.1%
 
1804-009598Y1< 0.1%
 
1210-012105Y1< 0.1%
 
1807-0175061< 0.1%
 
1508-006199Y1< 0.1%
 
1511-020605Y1< 0.1%
 
1906-005202Y1< 0.1%
 
1802-024002Y1< 0.1%
 
0609-005099Y1< 0.1%
 
1711-004523Y1< 0.1%
 
1502-003105Y1< 0.1%
 
1712-062419Y1< 0.1%
 
1111-009102Y1< 0.1%
 
0606-012605Y1< 0.1%
 
0812-013699Y1< 0.1%
 
1912-017998Y1< 0.1%
 
1805-009899Y1< 0.1%
 
0810-002899Y1< 0.1%
 
1706-040798Y1< 0.1%
 
0702-012817Y1< 0.1%
 
1810-024605Y1< 0.1%
 
1307-012405Y1< 0.1%
 
1705-004622Y1< 0.1%
 
1908-013105Y1< 0.1%
 
Other values (46457)4645799.9%
 
2020-12-12T20:59:16.591744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique46482 ?
Unique (%)100.0%
2020-12-12T20:59:16.680321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length11.87294006
Min length11

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
015784128.6%
 
18614515.6%
 
9478818.7%
 
-464828.4%
 
Y405747.4%
 
2364706.6%
 
5316125.7%
 
8247944.5%
 
6232074.2%
 
7223124.0%
 
3191303.5%
 
4154242.8%
 
u2< 0.1%
 
n2< 0.1%
 
k2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number46481684.2%
 
Dash Punctuation464828.4%
 
Uppercase Letter405747.4%
 
Lowercase Letter6< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
015784134.0%
 
18614518.5%
 
94788110.3%
 
2364707.8%
 
5316126.8%
 
8247945.3%
 
6232075.0%
 
7223124.8%
 
3191304.1%
 
4154243.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-46482100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y40574100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
u233.3%
 
n233.3%
 
k233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common51129892.6%
 
Latin405807.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
015784130.9%
 
18614516.8%
 
9478819.4%
 
-464829.1%
 
2364707.1%
 
5316126.2%
 
8247944.8%
 
6232074.5%
 
7223124.4%
 
3191303.7%
 
4154243.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y40574> 99.9%
 
u2< 0.1%
 
n2< 0.1%
 
k2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII551878100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
015784128.6%
 
18614515.6%
 
9478818.7%
 
-464828.4%
 
Y405747.4%
 
2364706.6%
 
5316125.7%
 
8247944.5%
 
6232074.2%
 
7223124.0%
 
3191303.5%
 
4154242.8%
 
u2< 0.1%
 
n2< 0.1%
 
k2< 0.1%
 

Primary
Categorical

MISSING

Distinct1
Distinct (%)< 0.1%
Missing5908
Missing (%)12.7%
Memory size363.3 KiB
Y
40574 
ValueCountFrequency (%) 
Y4057487.3%
 
(Missing)590812.7%
 
2020-12-12T20:59:16.759889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T20:59:16.805429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:16.857473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.254205929
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
Y4057469.6%
 
n1181620.3%
 
a590810.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter4057469.6%
 
Lowercase Letter1772430.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y40574100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1181666.7%
 
a590833.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin58298100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y4057469.6%
 
n1181620.3%
 
a590810.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII58298100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
Y4057469.6%
 
n1181620.3%
 
a590810.1%
 

Complaint Number
Categorical

HIGH CARDINALITY
UNIFORM

Distinct40587
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size363.3 KiB
1910-0300
 
10
1910-0216
 
10
1902-0343
 
10
1707-0245
 
10
1902-0342
 
10
Other values (40582)
46432 
ValueCountFrequency (%) 
1910-030010< 0.1%
 
1910-021610< 0.1%
 
1902-034310< 0.1%
 
1707-024510< 0.1%
 
1902-034210< 0.1%
 
1910-021710< 0.1%
 
1910-029910< 0.1%
 
1801-022310< 0.1%
 
1910-022010< 0.1%
 
1910-021810< 0.1%
 
1712-060910< 0.1%
 
1806-01139< 0.1%
 
1802-01759< 0.1%
 
1801-00849< 0.1%
 
1711-01939< 0.1%
 
1712-03579< 0.1%
 
1801-00859< 0.1%
 
1705-04738< 0.1%
 
1801-01478< 0.1%
 
1707-02738< 0.1%
 
1710-03548< 0.1%
 
1712-03558< 0.1%
 
1910-02198< 0.1%
 
1710-01938< 0.1%
 
1712-03038< 0.1%
 
Other values (40562)4625499.5%
 
2020-12-12T20:59:17.011606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique36436 ?
Unique (%)78.4%
2020-12-12T20:59:17.092175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length9
Min length9

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
013175031.5%
 
17837818.7%
 
-4648211.1%
 
2286716.9%
 
7216785.2%
 
8201514.8%
 
9201164.8%
 
6199974.8%
 
3180944.3%
 
5177544.2%
 
4152673.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number37185688.9%
 
Dash Punctuation4648211.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
013175035.4%
 
17837821.1%
 
2286717.7%
 
7216785.8%
 
8201515.4%
 
9201165.4%
 
6199975.4%
 
3180944.9%
 
5177544.8%
 
4152674.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-46482100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common418338100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
013175031.5%
 
17837818.7%
 
-4648211.1%
 
2286716.9%
 
7216785.2%
 
8201514.8%
 
9201164.8%
 
6199974.8%
 
3180944.3%
 
5177544.2%
 
4152673.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII418338100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
013175031.5%
 
17837818.7%
 
-4648211.1%
 
2286716.9%
 
7216785.2%
 
8201514.8%
 
9201164.8%
 
6199974.8%
 
3180944.3%
 
5177544.2%
 
4152673.6%
 

Complaint Item Type
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size2.3 MiB
Distinct26
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size363.3 KiB
alarm systems
13154 
uncategorized complaint
10632 
blocked exits
5026 
multiple fire code violations
3611 
extinguishers
3171 
Other values (21)
10886 
ValueCountFrequency (%) 
alarm systems1315428.3%
 
uncategorized complaint1063222.9%
 
blocked exits502610.8%
 
multiple fire code violations36117.8%
 
extinguishers31716.8%
 
sprinkler/standpipe systems25455.5%
 
combustible materials18163.9%
 
weeds and grass8991.9%
 
exit maintenance8071.7%
 
operating without a permit7801.7%
 
general hazardous materials6071.3%
 
ul cert verification4941.1%
 
fire escape4721.0%
 
electrical systems4090.9%
 
street numbering3900.8%
 
elevators not working3450.7%
 
overcrowded place of assembly3130.7%
 
hoarding2520.5%
 
roof access2250.5%
 
open vacant building1400.3%
 
unlicensed auto repair1260.3%
 
illegal occupancy970.2%
 
refused hood + duct service950.2%
 
crisp complaint inspection390.1%
 
unapproved place of assembly310.1%
 
2020-12-12T20:59:17.173245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T20:59:17.257317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length16
Mean length18.45867217
Min length3

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e8861510.3%
 
s786799.2%
 
a690588.0%
 
t676197.9%
 
i645937.5%
 
554666.5%
 
m501045.8%
 
l499815.8%
 
r467165.4%
 
o449735.2%
 
n409964.8%
 
c367734.3%
 
d256873.0%
 
p247962.9%
 
u222192.6%
 
g173212.0%
 
y165491.9%
 
z112391.3%
 
b95321.1%
 
x90041.0%
 
k79240.9%
 
f52410.6%
 
v50290.6%
 
h49050.6%
 
/25450.3%
 
Other values (2)24320.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter79989093.2%
 
Space Separator554666.5%
 
Other Punctuation25450.3%
 
Math Symbol95< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e8861511.1%
 
s786799.8%
 
a690588.6%
 
t676198.5%
 
i645938.1%
 
m501046.3%
 
l499816.2%
 
r467165.8%
 
o449735.6%
 
n409965.1%
 
c367734.6%
 
d256873.2%
 
p247963.1%
 
u222192.8%
 
g173212.2%
 
y165492.1%
 
z112391.4%
 
b95321.2%
 
x90041.1%
 
k79241.0%
 
f52410.7%
 
v50290.6%
 
h49050.6%
 
w23370.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
55466100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/2545100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+95100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin79989093.2%
 
Common581066.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e8861511.1%
 
s786799.8%
 
a690588.6%
 
t676198.5%
 
i645938.1%
 
m501046.3%
 
l499816.2%
 
r467165.8%
 
o449735.6%
 
n409965.1%
 
c367734.6%
 
d256873.2%
 
p247963.1%
 
u222192.8%
 
g173212.2%
 
y165492.1%
 
z112391.4%
 
b95321.2%
 
x90041.1%
 
k79241.0%
 
f52410.7%
 
v50290.6%
 
h49050.6%
 
w23370.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
5546695.5%
 
/25454.4%
 
+950.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII857996100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e8861510.3%
 
s786799.2%
 
a690588.0%
 
t676197.9%
 
i645937.5%
 
554666.5%
 
m501045.8%
 
l499815.8%
 
r467165.4%
 
o449735.2%
 
n409964.8%
 
c367734.3%
 
d256873.0%
 
p247962.9%
 
u222192.6%
 
g173212.0%
 
y165491.9%
 
z112391.3%
 
b95321.1%
 
x90041.0%
 
k79240.9%
 
f52410.6%
 
v50290.6%
 
h49050.6%
 
/25450.3%
 
Other values (2)24320.3%
 

311 Case Number
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1106
Distinct (%)98.0%
Missing45354
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean5623189.664
Minimum518310
Maximum12516302
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:17.340389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum518310
5-th percentile710765.1
Q12047782
median5534414
Q38567630.75
95-th percentile11602459.7
Maximum12516302
Range11997992
Interquartile range (IQR)6519848.75

Descriptive statistics

Standard deviation3585772.603
Coefficient of variation (CV)0.6376759129
Kurtosis-1.146543998
Mean5623189.664
Median Absolute Deviation (MAD)3104662.5
Skewness0.1417559831
Sum6342957941
Variance1.285776516e+13
MonotocityNot monotonic
2020-12-12T20:59:17.433969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
88636622< 0.1%
 
86237032< 0.1%
 
54728972< 0.1%
 
50842812< 0.1%
 
105169132< 0.1%
 
110127652< 0.1%
 
119788582< 0.1%
 
65598782< 0.1%
 
57909122< 0.1%
 
120860842< 0.1%
 
72999612< 0.1%
 
72959832< 0.1%
 
72946812< 0.1%
 
6095712< 0.1%
 
123927932< 0.1%
 
98208642< 0.1%
 
113937632< 0.1%
 
103350962< 0.1%
 
115102552< 0.1%
 
124988672< 0.1%
 
94523102< 0.1%
 
83872452< 0.1%
 
18771311< 0.1%
 
27046331< 0.1%
 
10038581< 0.1%
 
Other values (1081)10812.3%
 
(Missing)4535497.6%
 
ValueCountFrequency (%) 
5183101< 0.1%
 
5262641< 0.1%
 
5336701< 0.1%
 
5349091< 0.1%
 
5402281< 0.1%
 
5806031< 0.1%
 
5843451< 0.1%
 
5880841< 0.1%
 
5889361< 0.1%
 
5910911< 0.1%
 
ValueCountFrequency (%) 
125163021< 0.1%
 
124988672< 0.1%
 
124798541< 0.1%
 
124685681< 0.1%
 
124596751< 0.1%
 
124594511< 0.1%
 
124525651< 0.1%
 
124425311< 0.1%
 
124340181< 0.1%
 
124323291< 0.1%
 

Inspection Number
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct32639
Distinct (%)84.8%
Missing7999
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean214557.5609
Minimum8354
Maximum412269
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:17.547567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8354
5-th percentile23868.1
Q180324.5
median219308
Q3340571
95-th percentile396697.9
Maximum412269
Range403915
Interquartile range (IQR)260246.5

Descriptive statistics

Standard deviation132430.9624
Coefficient of variation (CV)0.6172281311
Kurtosis-1.541972802
Mean214557.5609
Median Absolute Deviation (MAD)129342
Skewness-0.07552427835
Sum8256818618
Variance1.753795979e+10
MonotocityNot monotonic
2020-12-12T20:59:17.640647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
39803010< 0.1%
 
34047010< 0.1%
 
33323210< 0.1%
 
37755210< 0.1%
 
37755110< 0.1%
 
39771610< 0.1%
 
39771710< 0.1%
 
39771910< 0.1%
 
39772310< 0.1%
 
39802910< 0.1%
 
34648910< 0.1%
 
3460029< 0.1%
 
3399009< 0.1%
 
3460039< 0.1%
 
3384739< 0.1%
 
3478109< 0.1%
 
3535009< 0.1%
 
3398988< 0.1%
 
3104628< 0.1%
 
3462658< 0.1%
 
3119388< 0.1%
 
3398068< 0.1%
 
3775508< 0.1%
 
3375058< 0.1%
 
3977228< 0.1%
 
Other values (32614)3825582.3%
 
(Missing)799917.2%
 
ValueCountFrequency (%) 
83541< 0.1%
 
83871< 0.1%
 
87271< 0.1%
 
87521< 0.1%
 
87561< 0.1%
 
87661< 0.1%
 
87701< 0.1%
 
88071< 0.1%
 
88081< 0.1%
 
88091< 0.1%
 
ValueCountFrequency (%) 
4122691< 0.1%
 
4122561< 0.1%
 
4122301< 0.1%
 
4122291< 0.1%
 
4122251< 0.1%
 
4122071< 0.1%
 
4121961< 0.1%
 
4121761< 0.1%
 
4121651< 0.1%
 
4121641< 0.1%
 

Address
Categorical

HIGH CARDINALITY

Distinct14077
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size363.3 KiB
1 Pier 39
 
61
3251 20th Ave
 
59
1251 Turk St
 
54
72 - 76 06th St
 
47
843 - 845 Market St
 
46
Other values (14072)
46215 
ValueCountFrequency (%) 
1 Pier 39610.1%
 
3251 20th Ave590.1%
 
1251 Turk St540.1%
 
72 - 76 06th St470.1%
 
843 - 845 Market St460.1%
 
268 - 272 Mcallister St460.1%
 
865 - 885 Market St460.1%
 
1 Ferry Building460.1%
 
255 Woodside Ave430.1%
 
1 Treasure Island Rd420.1%
 
20 12th St420.1%
 
1485 Pine St400.1%
 
1018 - 1024 Mission St390.1%
 
2280 - 2282 Mission St380.1%
 
2451 Sacramento St380.1%
 
32 - 40 06th St380.1%
 
2327 - 2329 Mission St370.1%
 
47 - 55 06th St370.1%
 
170 Ofarrell St360.1%
 
524 - 528 Valencia St360.1%
 
2000 - 2020 Market St360.1%
 
3315 - 3317 Mission St350.1%
 
450 - 464 Sutter St350.1%
 
2080 - 2086 Mission St340.1%
 
1000 Sutter St340.1%
 
Other values (14052)4543797.8%
 
2020-12-12T20:59:17.775764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6019 ?
Unique (%)12.9%
2020-12-12T20:59:17.867843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length17
Mean length17.27367583
Min length9

Overview of Unicode Properties

Unique unicode characters66
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
17636322.0%
 
t533366.6%
 
1390834.9%
 
S381514.8%
 
0318734.0%
 
e315103.9%
 
a283133.5%
 
2281073.5%
 
5255533.2%
 
3230412.9%
 
r229822.9%
 
n213412.7%
 
o212692.6%
 
4196722.5%
 
-172082.1%
 
i169682.1%
 
l162502.0%
 
6159182.0%
 
s148711.9%
 
7139871.7%
 
8139161.7%
 
9134781.7%
 
v107341.3%
 
h96721.2%
 
d86791.1%
 
Other values (41)9064011.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter29557936.8%
 
Decimal Number22462828.0%
 
Space Separator17636322.0%
 
Uppercase Letter8910811.1%
 
Dash Punctuation172082.1%
 
Other Punctuation29< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
13908317.4%
 
03187314.2%
 
22810712.5%
 
52555311.4%
 
32304110.3%
 
4196728.8%
 
6159187.1%
 
7139876.2%
 
8139166.2%
 
9134786.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
176363100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S3815142.8%
 
A85499.6%
 
M49745.6%
 
B49545.6%
 
C43614.9%
 
P41834.7%
 
G34583.9%
 
F22912.6%
 
H21152.4%
 
L20532.3%
 
V17462.0%
 
T16591.9%
 
E16471.8%
 
D15441.7%
 
W15211.7%
 
J14891.7%
 
N12191.4%
 
O11981.3%
 
R5600.6%
 
U4670.5%
 
K4450.5%
 
I3640.4%
 
Y890.1%
 
Q540.1%
 
Z17< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t5333618.0%
 
e3151010.7%
 
a283139.6%
 
r229827.8%
 
n213417.2%
 
o212697.2%
 
i169685.7%
 
l162505.5%
 
s148715.0%
 
v107343.6%
 
h96723.3%
 
d86792.9%
 
y68982.3%
 
c60772.1%
 
u59562.0%
 
k49131.7%
 
m39761.3%
 
g27890.9%
 
w27600.9%
 
f24150.8%
 
b18510.6%
 
p12080.4%
 
z4880.2%
 
j2160.1%
 
x94< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-17208100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1655.2%
 
.931.0%
 
:413.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common41822852.1%
 
Latin38468747.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
17636342.2%
 
1390839.3%
 
0318737.6%
 
2281076.7%
 
5255536.1%
 
3230415.5%
 
4196724.7%
 
-172084.1%
 
6159183.8%
 
7139873.3%
 
8139163.3%
 
9134783.2%
 
/16< 0.1%
 
.9< 0.1%
 
:4< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t5333613.9%
 
S381519.9%
 
e315108.2%
 
a283137.4%
 
r229826.0%
 
n213415.5%
 
o212695.5%
 
i169684.4%
 
l162504.2%
 
s148713.9%
 
v107342.8%
 
h96722.5%
 
d86792.3%
 
A85492.2%
 
y68981.8%
 
c60771.6%
 
u59561.5%
 
M49741.3%
 
B49541.3%
 
k49131.3%
 
C43611.1%
 
P41831.1%
 
m39761.0%
 
G34580.9%
 
g27890.7%
 
Other values (26)295237.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII802915100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
17636322.0%
 
t533366.6%
 
1390834.9%
 
S381514.8%
 
0318734.0%
 
e315103.9%
 
a283133.5%
 
2281073.5%
 
5255533.2%
 
3230412.9%
 
r229822.9%
 
n213412.7%
 
o212692.6%
 
4196722.5%
 
-172082.1%
 
i169682.1%
 
l162502.0%
 
6159182.0%
 
s148711.9%
 
7139871.7%
 
8139161.7%
 
9134781.7%
 
v107341.3%
 
h96721.2%
 
d86791.1%
 
Other values (41)9064011.3%
 

Zipcode
Real number (ℝ≥0)

Distinct27
Distinct (%)0.1%
Missing123
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean94113.94858
Minimum94102
Maximum94158
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:17.945409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum94102
5-th percentile94102
Q194108
median94111
Q394121
95-th percentile94133
Maximum94158
Range56
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.617665569
Coefficient of variation (CV)0.0001021917124
Kurtosis1.039974578
Mean94113.94858
Median Absolute Deviation (MAD)6
Skewness0.9716532163
Sum4363028542
Variance92.49949099
MonotocityNot monotonic
2020-12-12T20:59:18.016470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
94109557412.0%
 
9410240068.6%
 
9410339958.6%
 
9411038688.3%
 
9411527195.8%
 
9413326195.6%
 
9411723705.1%
 
9410823445.0%
 
9412322624.9%
 
9411818474.0%
 
9411416173.5%
 
9410715933.4%
 
9412115223.3%
 
9412213342.9%
 
9411213162.8%
 
9412412312.6%
 
9410510992.4%
 
9413110002.2%
 
941119472.0%
 
941166841.5%
 
941346821.5%
 
941326561.4%
 
941045161.1%
 
941272640.6%
 
941582070.4%
 
Other values (2)870.2%
 
(Missing)1230.3%
 
ValueCountFrequency (%) 
9410240068.6%
 
9410339958.6%
 
941045161.1%
 
9410510992.4%
 
9410715933.4%
 
9410823445.0%
 
94109557412.0%
 
9411038688.3%
 
941119472.0%
 
9411213162.8%
 
ValueCountFrequency (%) 
941582070.4%
 
941346821.5%
 
9413326195.6%
 
941326561.4%
 
9413110002.2%
 
94130810.2%
 
941296< 0.1%
 
941272640.6%
 
9412412312.6%
 
9412322624.9%
 

Battalion
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing13
Missing (%)< 0.1%
Memory size363.3 KiB
01
8687 
02
7550 
04
7053 
03
5308 
06
4362 
Other values (7)
13509 
ValueCountFrequency (%) 
01868718.7%
 
02755016.2%
 
04705315.2%
 
03530811.4%
 
0643629.4%
 
0534127.3%
 
0732647.0%
 
0824595.3%
 
1022664.9%
 
0920394.4%
 
AP600.1%
 
009< 0.1%
 
(Missing)13< 0.1%
 
2020-12-12T20:59:18.102044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T20:59:18.172605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.000279678
Min length2

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
04641849.9%
 
11095311.8%
 
275508.1%
 
470537.6%
 
353085.7%
 
643624.7%
 
534123.7%
 
732643.5%
 
824592.6%
 
920392.2%
 
A600.1%
 
P600.1%
 
n26< 0.1%
 
a13< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9281899.8%
 
Uppercase Letter1200.1%
 
Lowercase Letter39< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
04641850.0%
 
11095311.8%
 
275508.1%
 
470537.6%
 
353085.7%
 
643624.7%
 
534123.7%
 
732643.5%
 
824592.6%
 
920392.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2666.7%
 
a1333.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A6050.0%
 
P6050.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common9281899.8%
 
Latin1590.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
04641850.0%
 
11095311.8%
 
275508.1%
 
470537.6%
 
353085.7%
 
643624.7%
 
534123.7%
 
732643.5%
 
824592.6%
 
920392.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A6037.7%
 
P6037.7%
 
n2616.4%
 
a138.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII92977100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
04641849.9%
 
11095311.8%
 
275508.1%
 
470537.6%
 
353085.7%
 
643624.7%
 
534123.7%
 
732643.5%
 
824592.6%
 
920392.2%
 
A600.1%
 
P600.1%
 
n26< 0.1%
 
a13< 0.1%
 

Station Area
Categorical

MISSING

Distinct48
Distinct (%)0.1%
Missing482
Missing (%)1.0%
Memory size363.3 KiB
01
4465 
03
3501 
07
 
2809
02
 
2516
16
 
2416
Other values (43)
30293 
ValueCountFrequency (%) 
0144659.6%
 
0335017.5%
 
0728096.0%
 
0225165.4%
 
1624165.2%
 
4122044.7%
 
3620504.4%
 
3817763.8%
 
1317543.8%
 
0614673.2%
 
2114653.2%
 
0513372.9%
 
3113092.8%
 
2812652.7%
 
1012462.7%
 
1111702.5%
 
089912.1%
 
298781.9%
 
127931.7%
 
147731.7%
 
356991.5%
 
246521.4%
 
226271.3%
 
155741.2%
 
345701.2%
 
Other values (23)669314.4%
 
2020-12-12T20:59:18.258178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T20:59:18.335745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.010434147
Min length2

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
12085122.3%
 
01975921.1%
 
31452315.5%
 
21097511.7%
 
663066.7%
 
462356.7%
 
846054.9%
 
736813.9%
 
529553.2%
 
919902.1%
 
n9641.0%
 
a4820.5%
 
A600.1%
 
P600.1%
 
E3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9188098.3%
 
Lowercase Letter14461.5%
 
Uppercase Letter1230.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
12085122.7%
 
01975921.5%
 
31452315.8%
 
21097511.9%
 
663066.9%
 
462356.8%
 
846055.0%
 
736814.0%
 
529553.2%
 
919902.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n96466.7%
 
a48233.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A6048.8%
 
P6048.8%
 
E32.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common9188098.3%
 
Latin15691.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
12085122.7%
 
01975921.5%
 
31452315.8%
 
21097511.9%
 
663066.9%
 
462356.8%
 
846055.0%
 
736814.0%
 
529553.2%
 
919902.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n96461.4%
 
a48230.7%
 
A603.8%
 
P603.8%
 
E30.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII93449100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
12085122.3%
 
01975921.1%
 
31452315.5%
 
21097511.7%
 
663066.7%
 
462356.7%
 
846054.9%
 
736813.9%
 
529553.2%
 
919902.1%
 
n9641.0%
 
a4820.5%
 
A600.1%
 
P600.1%
 
E3< 0.1%
 

Fire Prevention District
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size363.3 KiB
04
7013 
06
4342 
05
4061 
01N
3963 
01W
3902 
Other values (12)
23192 
ValueCountFrequency (%) 
04701315.1%
 
0643429.3%
 
0540618.7%
 
01N39638.5%
 
01W39028.4%
 
02N35867.7%
 
02S34057.3%
 
0732627.0%
 
03W28686.2%
 
0824495.3%
 
1022594.9%
 
0920394.4%
 
03S13482.9%
 
03N10922.3%
 
01S7881.7%
 
AP600.1%
 
60360.1%
 
(Missing)9< 0.1%
 
2020-12-12T20:59:18.414813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T20:59:18.492880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.450948754
Min length2

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
04641340.7%
 
1109129.6%
 
N86417.6%
 
470136.2%
 
269916.1%
 
W67705.9%
 
S55414.9%
 
353084.7%
 
643783.8%
 
540613.6%
 
732622.9%
 
824492.1%
 
920391.8%
 
A600.1%
 
P600.1%
 
n18< 0.1%
 
a9< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9282681.5%
 
Uppercase Letter2107218.5%
 
Lowercase Letter27< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
04641350.0%
 
11091211.8%
 
470137.6%
 
269917.5%
 
353085.7%
 
643784.7%
 
540614.4%
 
732623.5%
 
824492.6%
 
920392.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N864141.0%
 
W677032.1%
 
S554126.3%
 
A600.3%
 
P600.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1866.7%
 
a933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common9282681.5%
 
Latin2109918.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
04641350.0%
 
11091211.8%
 
470137.6%
 
269917.5%
 
353085.7%
 
643784.7%
 
540614.4%
 
732623.5%
 
824492.6%
 
920392.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N864141.0%
 
W677032.1%
 
S554126.3%
 
A600.3%
 
P600.3%
 
n180.1%
 
a9< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII113925100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
04641340.7%
 
1109129.6%
 
N86417.6%
 
470136.2%
 
269916.1%
 
W67705.9%
 
S55414.9%
 
353084.7%
 
643783.8%
 
540613.6%
 
732622.9%
 
824492.1%
 
920391.8%
 
A600.1%
 
P600.1%
 
n18< 0.1%
 
a9< 0.1%
 

Received Date
Categorical

HIGH CARDINALITY

Distinct4447
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size363.3 KiB
12/18/2017
 
92
12/15/2017
 
83
10/15/2019
 
72
08/29/2017
 
60
08/01/2016
 
58
Other values (4442)
46117 
ValueCountFrequency (%) 
12/18/2017920.2%
 
12/15/2017830.2%
 
10/15/2019720.2%
 
08/29/2017600.1%
 
08/01/2016580.1%
 
12/12/2017560.1%
 
12/14/2017560.1%
 
12/19/2017550.1%
 
02/05/2018540.1%
 
12/28/2017530.1%
 
05/23/2019530.1%
 
11/02/2017530.1%
 
06/08/2018520.1%
 
11/02/2015520.1%
 
07/11/2007520.1%
 
01/19/2006520.1%
 
03/05/2007500.1%
 
06/20/2006500.1%
 
12/08/2017490.1%
 
05/10/2017490.1%
 
06/28/2016480.1%
 
12/21/2017480.1%
 
10/03/2018470.1%
 
12/20/2017460.1%
 
01/24/2006460.1%
 
Other values (4422)4509697.0%
 
2020-12-12T20:59:18.587962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique341 ?
Unique (%)0.7%
2020-12-12T20:59:18.666530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
012035025.9%
 
/9296420.0%
 
27714116.6%
 
17354215.8%
 
7174563.8%
 
8164333.5%
 
9164303.5%
 
6155343.3%
 
5129182.8%
 
3125342.7%
 
495182.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number37185680.0%
 
Other Punctuation9296420.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
012035032.4%
 
27714120.7%
 
17354219.8%
 
7174564.7%
 
8164334.4%
 
9164304.4%
 
6155344.2%
 
5129183.5%
 
3125343.4%
 
495182.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/92964100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common464820100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
012035025.9%
 
/9296420.0%
 
27714116.6%
 
17354215.8%
 
7174563.8%
 
8164333.5%
 
9164303.5%
 
6155343.3%
 
5129182.8%
 
3125342.7%
 
495182.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII464820100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
012035025.9%
 
/9296420.0%
 
27714116.6%
 
17354215.8%
 
7174563.8%
 
8164333.5%
 
9164303.5%
 
6155343.3%
 
5129182.8%
 
3125342.7%
 
495182.0%
 

Entry Date
Categorical

HIGH CARDINALITY

Distinct4190
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size363.3 KiB
12/18/2017
 
90
12/15/2017
 
82
10/15/2019
 
71
12/19/2017
 
67
08/29/2017
 
60
Other values (4185)
46112 
ValueCountFrequency (%) 
12/18/2017900.2%
 
12/15/2017820.2%
 
10/15/2019710.2%
 
12/19/2017670.1%
 
08/29/2017600.1%
 
08/01/2016600.1%
 
11/02/2017580.1%
 
01/04/2018570.1%
 
07/27/2005550.1%
 
12/12/2017550.1%
 
12/14/2017550.1%
 
06/08/2018530.1%
 
01/19/2006520.1%
 
02/05/2018520.1%
 
03/05/2007520.1%
 
12/28/2017520.1%
 
06/20/2006520.1%
 
05/10/2017480.1%
 
12/08/2017480.1%
 
12/21/2017480.1%
 
10/03/2018470.1%
 
11/02/2015470.1%
 
07/11/2007470.1%
 
06/28/2016470.1%
 
02/01/2018470.1%
 
Other values (4165)4508097.0%
 
2020-12-12T20:59:18.758609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique255 ?
Unique (%)0.5%
2020-12-12T20:59:18.836176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
012012225.8%
 
/9296420.0%
 
27730616.6%
 
17372815.9%
 
7175583.8%
 
9163803.5%
 
8163223.5%
 
6156133.4%
 
5129392.8%
 
3124572.7%
 
494312.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number37185680.0%
 
Other Punctuation9296420.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
012012232.3%
 
27730620.8%
 
17372819.8%
 
7175584.7%
 
9163804.4%
 
8163224.4%
 
6156134.2%
 
5129393.5%
 
3124573.3%
 
494312.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/92964100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common464820100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
012012225.8%
 
/9296420.0%
 
27730616.6%
 
17372815.9%
 
7175583.8%
 
9163803.5%
 
8163223.5%
 
6156133.4%
 
5129392.8%
 
3124572.7%
 
494312.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII464820100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
012012225.8%
 
/9296420.0%
 
27730616.6%
 
17372815.9%
 
7175583.8%
 
9163803.5%
 
8163223.5%
 
6156133.4%
 
5129392.8%
 
3124572.7%
 
494312.0%
 

Disposition
Categorical

MISSING

Distinct11
Distinct (%)< 0.1%
Missing2010
Missing (%)4.3%
Memory size363.3 KiB
condition corrected
22462 
violation issued
11005 
no merit
8840 
duplicate complaint
 
718
referred to dbi
 
435
Other values (6)
 
1012
ValueCountFrequency (%) 
condition corrected2246248.3%
 
violation issued1100523.7%
 
no merit884019.0%
 
duplicate complaint7181.5%
 
referred to dbi4350.9%
 
referred to another agency4210.9%
 
no jurisdiction3130.7%
 
no access to building2070.4%
 
referred to pm inspection task force390.1%
 
referred to dph18< 0.1%
 
citation issued14< 0.1%
 
(Missing)20104.3%
 
2020-12-12T20:59:18.910240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T20:59:18.984804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length19
Mean length15.52530012
Min length3

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10142014.1%
 
i9012312.5%
 
n714819.9%
 
c700629.7%
 
e693679.6%
 
t681659.4%
 
d585478.1%
 
r572767.9%
 
463376.4%
 
s228433.2%
 
a155532.2%
 
l126481.8%
 
u122571.7%
 
v110051.5%
 
m95971.3%
 
p15320.2%
 
f9520.1%
 
b6420.1%
 
g6280.1%
 
h4390.1%
 
y4210.1%
 
j313< 0.1%
 
k39< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter67531093.6%
 
Space Separator463376.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10142015.0%
 
i9012313.3%
 
n7148110.6%
 
c7006210.4%
 
e6936710.3%
 
t6816510.1%
 
d585478.7%
 
r572768.5%
 
s228433.4%
 
a155532.3%
 
l126481.9%
 
u122571.8%
 
v110051.6%
 
m95971.4%
 
p15320.2%
 
f9520.1%
 
b6420.1%
 
g6280.1%
 
h4390.1%
 
y4210.1%
 
j313< 0.1%
 
k39< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
46337100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin67531093.6%
 
Common463376.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10142015.0%
 
i9012313.3%
 
n7148110.6%
 
c7006210.4%
 
e6936710.3%
 
t6816510.1%
 
d585478.7%
 
r572768.5%
 
s228433.4%
 
a155532.3%
 
l126481.9%
 
u122571.8%
 
v110051.6%
 
m95971.4%
 
p15320.2%
 
f9520.1%
 
b6420.1%
 
g6280.1%
 
h4390.1%
 
y4210.1%
 
j313< 0.1%
 
k39< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
46337100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII721647100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10142014.1%
 
i9012312.5%
 
n714819.9%
 
c700629.7%
 
e693679.6%
 
t681659.4%
 
d585478.1%
 
r572767.9%
 
463376.4%
 
s228433.2%
 
a155532.2%
 
l126481.8%
 
u122571.7%
 
v110051.5%
 
m95971.3%
 
p15320.2%
 
f9520.1%
 
b6420.1%
 
g6280.1%
 
h4390.1%
 
y4210.1%
 
j313< 0.1%
 
k39< 0.1%
 

Disposition Date
Categorical

HIGH CARDINALITY
MISSING

Distinct4008
Distinct (%)9.1%
Missing2495
Missing (%)5.4%
Memory size363.3 KiB
01/03/2018
 
237
03/22/2015
 
228
01/02/2018
 
176
09/30/2015
 
95
01/10/2018
 
83
Other values (4003)
43168 
ValueCountFrequency (%) 
01/03/20182370.5%
 
03/22/20152280.5%
 
01/02/20181760.4%
 
09/30/2015950.2%
 
01/10/2018830.2%
 
01/04/2018740.2%
 
05/24/2007710.2%
 
01/07/2009590.1%
 
12/19/2017580.1%
 
02/14/2018530.1%
 
06/20/2006510.1%
 
09/27/2018490.1%
 
01/19/2006490.1%
 
01/05/2018490.1%
 
08/28/2018490.1%
 
01/31/2006480.1%
 
02/27/2017470.1%
 
01/16/2020460.1%
 
08/31/2016460.1%
 
09/19/2018450.1%
 
02/23/2015450.1%
 
02/19/2020450.1%
 
10/23/2019440.1%
 
03/12/2020440.1%
 
08/27/2018440.1%
 
Other values (3983)4215290.7%
 
(Missing)24955.4%
 
2020-12-12T20:59:19.082888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique256 ?
Unique (%)0.6%
2020-12-12T20:59:19.163458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.624263156
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
011392425.5%
 
/8797419.7%
 
27386816.5%
 
16979515.6%
 
8160323.6%
 
9157763.5%
 
7155813.5%
 
6138903.1%
 
3125142.8%
 
5117462.6%
 
487702.0%
 
n49901.1%
 
a24950.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number35189678.7%
 
Other Punctuation8797419.7%
 
Lowercase Letter74851.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
011392432.4%
 
27386821.0%
 
16979519.8%
 
8160324.6%
 
9157764.5%
 
7155814.4%
 
6138903.9%
 
3125143.6%
 
5117463.3%
 
487702.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/87974100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n499066.7%
 
a249533.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common43987098.3%
 
Latin74851.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
011392425.9%
 
/8797420.0%
 
27386816.8%
 
16979515.9%
 
8160323.6%
 
9157763.6%
 
7155813.5%
 
6138903.2%
 
3125142.8%
 
5117462.7%
 
487702.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n499066.7%
 
a249533.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII447355100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
011392425.5%
 
/8797419.7%
 
27386816.5%
 
16979515.6%
 
8160323.6%
 
9157763.5%
 
7155813.5%
 
6138903.1%
 
3125142.8%
 
5117462.6%
 
487702.0%
 
n49901.1%
 
a24950.6%
 

Supervisor District
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing118
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.213657148
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:19.231516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.735661737
Coefficient of variation (CV)0.5247107088
Kurtosis-0.9400946204
Mean5.213657148
Median Absolute Deviation (MAD)2
Skewness0.3177180855
Sum241726
Variance7.483845141
MonotocityNot monotonic
2020-12-12T20:59:19.293569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
3971220.9%
 
6896119.3%
 
2539011.6%
 
5532911.5%
 
939318.5%
 
836797.9%
 
128506.1%
 
1025075.4%
 
715633.4%
 
413052.8%
 
1111372.4%
 
(Missing)1180.3%
 
ValueCountFrequency (%) 
128506.1%
 
2539011.6%
 
3971220.9%
 
413052.8%
 
5532911.5%
 
6896119.3%
 
715633.4%
 
836797.9%
 
939318.5%
 
1025075.4%
 
ValueCountFrequency (%) 
1111372.4%
 
1025075.4%
 
939318.5%
 
836797.9%
 
715633.4%
 
6896119.3%
 
5532911.5%
 
413052.8%
 
3971220.9%
 
2539011.6%
 
Distinct41
Distinct (%)0.1%
Missing122
Missing (%)0.3%
Memory size363.3 KiB
Mission
4364 
Tenderloin
4230 
Financial District/South Beach
3609 
Nob Hill
2972 
South of Market
 
2799
Other values (36)
28386 
ValueCountFrequency (%) 
Mission43649.4%
 
Tenderloin42309.1%
 
Financial District/South Beach36097.8%
 
Nob Hill29726.4%
 
South of Market27996.0%
 
Pacific Heights21164.6%
 
Marina20374.4%
 
Chinatown20254.4%
 
Western Addition17123.7%
 
Outer Richmond16163.5%
 
Hayes Valley15423.3%
 
North Beach13392.9%
 
Russian Hill13392.9%
 
Bayview Hunters Point13382.9%
 
Sunset/Parkside12952.8%
 
Castro/Upper Market12692.7%
 
Inner Richmond9852.1%
 
Haight Ashbury9782.1%
 
Inner Sunset8231.8%
 
Lone Mountain/USF7411.6%
 
Bernal Heights7391.6%
 
Potrero Hill6791.5%
 
West of Twin Peaks6331.4%
 
Noe Valley6251.3%
 
Presidio Heights5891.3%
 
Other values (16)39668.5%
 
2020-12-12T20:59:19.377141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T20:59:19.459212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length12
Mean length13.4059636
Min length3

Overview of Unicode Properties

Unique unicode characters46
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i6332810.2%
 
n490737.9%
 
e486927.8%
 
a403066.5%
 
t399076.4%
 
394546.3%
 
o388906.2%
 
s346395.6%
 
r318125.1%
 
l248974.0%
 
h231553.7%
 
c205913.3%
 
u150262.4%
 
d130292.1%
 
M126092.0%
 
H122922.0%
 
S93141.5%
 
P76821.2%
 
/76781.2%
 
B75951.2%
 
k71281.1%
 
y67991.1%
 
f56420.9%
 
T53590.9%
 
w50860.8%
 
Other values (21)531538.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter48442277.7%
 
Uppercase Letter9158214.7%
 
Space Separator394546.3%
 
Other Punctuation76781.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M1260913.8%
 
H1229213.4%
 
S931410.2%
 
P76828.4%
 
B75958.3%
 
T53595.9%
 
N49365.4%
 
F43504.7%
 
R39404.3%
 
D36093.9%
 
C32943.6%
 
A26902.9%
 
V25752.8%
 
O24052.6%
 
W23452.6%
 
I22712.5%
 
U20102.2%
 
L12361.3%
 
E5160.6%
 
J2930.3%
 
G2610.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i6332813.1%
 
n4907310.1%
 
e4869210.1%
 
a403068.3%
 
t399078.2%
 
o388908.0%
 
s346397.2%
 
r318126.6%
 
l248975.1%
 
h231554.8%
 
c205914.3%
 
u150263.1%
 
d130292.7%
 
k71281.5%
 
y67991.4%
 
f56421.2%
 
w50861.0%
 
g48041.0%
 
b39500.8%
 
p28310.6%
 
m26010.5%
 
v17200.4%
 
x5160.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
39454100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/7678100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin57600492.4%
 
Common471327.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i6332811.0%
 
n490738.5%
 
e486928.5%
 
a403067.0%
 
t399076.9%
 
o388906.8%
 
s346396.0%
 
r318125.5%
 
l248974.3%
 
h231554.0%
 
c205913.6%
 
u150262.6%
 
d130292.3%
 
M126092.2%
 
H122922.1%
 
S93141.6%
 
P76821.3%
 
B75951.3%
 
k71281.2%
 
y67991.2%
 
f56421.0%
 
T53590.9%
 
w50860.9%
 
N49360.9%
 
g48040.8%
 
Other values (19)434137.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
3945483.7%
 
/767816.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII623136100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i6332810.2%
 
n490737.9%
 
e486927.8%
 
a403066.5%
 
t399076.4%
 
394546.3%
 
o388906.2%
 
s346395.6%
 
r318125.1%
 
l248974.0%
 
h231553.7%
 
c205913.3%
 
u150262.4%
 
d130292.1%
 
M126092.0%
 
H122922.0%
 
S93141.5%
 
P76821.2%
 
/76781.2%
 
B75951.2%
 
k71281.1%
 
y67991.1%
 
f56420.9%
 
T53590.9%
 
w50860.8%
 
Other values (21)531538.5%
 

Location
Categorical

HIGH CARDINALITY

Distinct13667
Distinct (%)29.4%
Missing52
Missing (%)0.1%
Memory size363.3 KiB
POINT (-122.409848 37.80887100002967)
 
61
POINT (-122.47682576000001 37.72847060002962)
 
59
POINT (-122.42950406000001 37.78046422002965)
 
54
POINT (-122.40935915 37.78104761002967)
 
47
POINT (-122.40614459000001 37.78410039002966)
 
46
Other values (13662)
46163 
ValueCountFrequency (%) 
POINT (-122.409848 37.80887100002967)610.1%
 
POINT (-122.47682576000001 37.72847060002962)590.1%
 
POINT (-122.42950406000001 37.78046422002965)540.1%
 
POINT (-122.40935915 37.78104761002967)470.1%
 
POINT (-122.40614459000001 37.78410039002966)460.1%
 
POINT (-122.41651556 37.78085577002967)460.1%
 
POINT (-122.39341300000001 37.79576400002966)460.1%
 
POINT (-122.40715834 37.783946920029656)460.1%
 
POINT (-122.48090814 37.71826626002961)440.1%
 
POINT (-122.45523285 37.746582390029616)430.1%
 
POINT (-122.37134200000001 37.81705500002968)420.1%
 
POINT (-122.47930095999999 37.72693770002961)420.1%
 
POINT (-122.42002629 37.77391508002965)420.1%
 
POINT (-122.39988300000002 37.79642778002967)400.1%
 
POINT (-122.42006898 37.789417420029665)400.1%
 
POINT (-122.40945227 37.78070398002966)390.1%
 
POINT (-122.43312617999999 37.78964174002967)380.1%
 
POINT (-122.40985673 37.78145858002965)380.1%
 
POINT (-122.41954049 37.760573090029645)380.1%
 
POINT (-122.40922245000002 37.78168697002967)370.1%
 
POINT (-122.41880687999999 37.75978022002965)370.1%
 
POINT (-122.42699098999998 37.769404140029636)360.1%
 
POINT (-122.42213594 37.76441479002964)360.1%
 
POINT (-122.40736346000001 37.78679591002967)360.1%
 
POINT (-122.47317011 37.7160294500296)350.1%
 
Other values (13642)4536297.6%
 
(Missing)520.1%
 
2020-12-12T20:59:19.579315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5749 ?
Unique (%)12.4%
2020-12-12T20:59:19.665890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length46
Median length40
Mean length41.79944925
Min length3

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
023133711.9%
 
220985210.8%
 
71601328.2%
 
91519667.8%
 
61258266.5%
 
11220156.3%
 
31168076.0%
 
41129225.8%
 
928604.8%
 
.928604.8%
 
8783524.0%
 
5763973.9%
 
P464302.4%
 
O464302.4%
 
I464302.4%
 
N464302.4%
 
T464302.4%
 
(464302.4%
 
-464302.4%
 
)464302.4%
 
n104< 0.1%
 
a52< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number138560671.3%
 
Uppercase Letter23215011.9%
 
Space Separator928604.8%
 
Other Punctuation928604.8%
 
Open Punctuation464302.4%
 
Dash Punctuation464302.4%
 
Close Punctuation464302.4%
 
Lowercase Letter156< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P4643020.0%
 
O4643020.0%
 
I4643020.0%
 
N4643020.0%
 
T4643020.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
92860100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(46430100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-46430100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
023133716.7%
 
220985215.1%
 
716013211.6%
 
915196611.0%
 
61258269.1%
 
11220158.8%
 
31168078.4%
 
41129228.1%
 
8783525.7%
 
5763975.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.92860100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)46430100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n10466.7%
 
a5233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common171061688.0%
 
Latin23230612.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
P4643020.0%
 
O4643020.0%
 
I4643020.0%
 
N4643020.0%
 
T4643020.0%
 
n104< 0.1%
 
a52< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
023133713.5%
 
220985212.3%
 
71601329.4%
 
91519668.9%
 
61258267.4%
 
11220157.1%
 
31168076.8%
 
41129226.6%
 
928605.4%
 
.928605.4%
 
8783524.6%
 
5763974.5%
 
(464302.7%
 
-464302.7%
 
)464302.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1942922100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
023133711.9%
 
220985210.8%
 
71601328.2%
 
91519667.8%
 
61258266.5%
 
11220156.3%
 
31168076.0%
 
41129225.8%
 
928604.8%
 
.928604.8%
 
8783524.0%
 
5763973.9%
 
P464302.4%
 
O464302.4%
 
I464302.4%
 
N464302.4%
 
T464302.4%
 
(464302.4%
 
-464302.4%
 
)464302.4%
 
n104< 0.1%
 
a52< 0.1%
 

Neighborhoods_from_fyvs_ahh9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)0.1%
Missing122
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean20.89199741
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:19.743457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median21
Q332
95-th percentile40
Maximum41
Range40
Interquartile range (IQR)22

Descriptive statistics

Standard deviation11.93287653
Coefficient of variation (CV)0.5711697306
Kurtosis-1.199340319
Mean20.89199741
Median Absolute Deviation (MAD)11
Skewness-0.03297336068
Sum968553
Variance142.3935424
MonotocityNot monotonic
2020-12-12T20:59:19.826528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%) 
1944469.6%
 
3642309.1%
 
636097.8%
 
2129726.4%
 
3427996.0%
 
2721164.6%
 
1720374.4%
 
420254.4%
 
4117123.7%
 
2616163.5%
 
1015423.3%
 
2313392.9%
 
3213392.9%
 
113382.9%
 
3512952.8%
 
312692.7%
 
119852.1%
 
99782.1%
 
128231.8%
 
167411.6%
 
27391.6%
 
406331.4%
 
226251.3%
 
295971.3%
 
315891.3%
 
Other values (16)39668.5%
 
ValueCountFrequency (%) 
113382.9%
 
27391.6%
 
312692.7%
 
420254.4%
 
55161.1%
 
636097.8%
 
71830.4%
 
8390.1%
 
99782.1%
 
1015423.3%
 
ValueCountFrequency (%) 
4117123.7%
 
406331.4%
 
392040.4%
 
384150.9%
 
37810.2%
 
3642309.1%
 
3512952.8%
 
3427996.0%
 
33470.1%
 
3213392.9%
 

Supervisor Districts
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing118
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean7.126973514
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:19.903594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile11
Maximum11
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.369441697
Coefficient of variation (CV)0.472773147
Kurtosis-0.9679949282
Mean7.126973514
Median Absolute Deviation (MAD)2
Skewness-0.6765807404
Sum330435
Variance11.35313735
MonotocityNot monotonic
2020-12-12T20:59:19.965648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
10971220.9%
 
9896119.3%
 
1539011.6%
 
11532911.5%
 
739318.5%
 
536797.9%
 
228506.1%
 
825075.4%
 
415633.4%
 
313052.8%
 
611372.4%
 
(Missing)1180.3%
 
ValueCountFrequency (%) 
1539011.6%
 
228506.1%
 
313052.8%
 
415633.4%
 
536797.9%
 
611372.4%
 
739318.5%
 
825075.4%
 
9896119.3%
 
10971220.9%
 
ValueCountFrequency (%) 
11532911.5%
 
10971220.9%
 
9896119.3%
 
825075.4%
 
739318.5%
 
611372.4%
 
536797.9%
 
415633.4%
 
313052.8%
 
228506.1%
 

Fire Prevention Districts
Real number (ℝ≥0)

MISSING

Distinct15
Distinct (%)< 0.1%
Missing520
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean8.37687655
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:20.029703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q313
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.550578911
Coefficient of variation (CV)0.5432309863
Kurtosis-1.345048169
Mean8.37687655
Median Absolute Deviation (MAD)5
Skewness-0.1036959347
Sum385018
Variance20.70776842
MonotocityNot monotonic
2020-12-12T20:59:20.095259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
13693814.9%
 
243329.3%
 
1540878.8%
 
538678.3%
 
337878.1%
 
735887.7%
 
834067.3%
 
1133137.1%
 
1428176.1%
 
124495.3%
 
1022514.8%
 
920214.3%
 
613672.9%
 
129512.0%
 
47881.7%
 
(Missing)5201.1%
 
ValueCountFrequency (%) 
124495.3%
 
243329.3%
 
337878.1%
 
47881.7%
 
538678.3%
 
613672.9%
 
735887.7%
 
834067.3%
 
920214.3%
 
1022514.8%
 
ValueCountFrequency (%) 
1540878.8%
 
1428176.1%
 
13693814.9%
 
129512.0%
 
1133137.1%
 
1022514.8%
 
920214.3%
 
834067.3%
 
735887.7%
 
613672.9%
 

Current Police Districts
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing119
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.065742079
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:20.166321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.969952984
Coefficient of variation (CV)0.5862819184
Kurtosis-1.291186256
Mean5.065742079
Median Absolute Deviation (MAD)3
Skewness0.003480331381
Sum234863
Variance8.820620727
MonotocityNot monotonic
2020-12-12T20:59:20.225371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1878218.9%
 
6803517.3%
 
2535511.5%
 
7513611.0%
 
942019.0%
 
835907.7%
 
1031926.9%
 
529376.3%
 
426705.7%
 
324655.3%
 
(Missing)1190.3%
 
ValueCountFrequency (%) 
1878218.9%
 
2535511.5%
 
324655.3%
 
426705.7%
 
529376.3%
 
6803517.3%
 
7513611.0%
 
835907.7%
 
942019.0%
 
1031926.9%
 
ValueCountFrequency (%) 
1031926.9%
 
942019.0%
 
835907.7%
 
7513611.0%
 
6803517.3%
 
529376.3%
 
426705.7%
 
324655.3%
 
2535511.5%
 
1878218.9%
 

Neighborhoods - Analysis Boundaries
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)0.1%
Missing122
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean21.04324849
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:20.300436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median21
Q332
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)23

Descriptive statistics

Standard deviation11.91155768
Coefficient of variation (CV)0.5660512772
Kurtosis-1.322525797
Mean21.04324849
Median Absolute Deviation (MAD)12
Skewness-0.05130915762
Sum975565
Variance141.8852065
MonotocityNot monotonic
2020-12-12T20:59:20.382006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%) 
2044469.6%
 
3642309.1%
 
836097.8%
 
2129726.4%
 
3427996.0%
 
3021164.6%
 
1320374.4%
 
620254.4%
 
3917123.7%
 
2916163.5%
 
915423.3%
 
3213392.9%
 
2313392.9%
 
113382.9%
 
3512952.8%
 
512692.7%
 
119852.1%
 
39782.1%
 
148231.8%
 
187411.6%
 
27391.6%
 
416331.4%
 
226251.3%
 
265971.3%
 
315891.3%
 
Other values (16)39668.5%
 
ValueCountFrequency (%) 
113382.9%
 
27391.6%
 
39782.1%
 
45701.2%
 
512692.7%
 
620254.4%
 
75161.1%
 
836097.8%
 
915423.3%
 
101830.4%
 
ValueCountFrequency (%) 
416331.4%
 
402040.4%
 
3917123.7%
 
384150.9%
 
37810.2%
 
3642309.1%
 
3512952.8%
 
3427996.0%
 
33470.1%
 
3213392.9%
 

Zip Codes
Real number (ℝ≥0)

Distinct29
Distinct (%)0.1%
Missing52
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean20487.68553
Minimum54
Maximum29492
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:20.466079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile55
Q1308
median28855
Q328859
95-th percentile29492
Maximum29492
Range29438
Interquartile range (IQR)28551

Descriptive statistics

Standard deviation13145.12623
Coefficient of variation (CV)0.6416110894
Kurtosis-1.182771031
Mean20487.68553
Median Absolute Deviation (MAD)5
Skewness-0.9033168905
Sum951243239
Variance172794343.6
MonotocityNot monotonic
2020-12-12T20:59:20.539141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
28858560212.1%
 
2885241859.0%
 
2885339608.5%
 
2885938838.4%
 
2949027655.9%
 
30826605.7%
 
2949224235.2%
 
2885722854.9%
 
5720144.3%
 
5418514.0%
 
2885616073.5%
 
2886215993.4%
 
5515303.3%
 
5613182.8%
 
5812802.8%
 
2886112762.7%
 
2885510492.3%
 
639342.0%
 
288608541.8%
 
294916941.5%
 
3096601.4%
 
646401.4%
 
288545941.3%
 
593540.8%
 
3102650.6%
 
Other values (4)1480.3%
 
ValueCountFrequency (%) 
5418514.0%
 
5515303.3%
 
5613182.8%
 
5720144.3%
 
5812802.8%
 
593540.8%
 
60600.1%
 
616< 0.1%
 
62810.2%
 
639342.0%
 
ValueCountFrequency (%) 
2949224235.2%
 
294916941.5%
 
2949027655.9%
 
2886215993.4%
 
2886112762.7%
 
288608541.8%
 
2885938838.4%
 
28858560212.1%
 
2885722854.9%
 
2885616073.5%
 

Neighborhoods (old)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)0.1%
Missing122
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean20.89199741
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:20.623714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median21
Q332
95-th percentile40
Maximum41
Range40
Interquartile range (IQR)22

Descriptive statistics

Standard deviation11.93287653
Coefficient of variation (CV)0.5711697306
Kurtosis-1.199340319
Mean20.89199741
Median Absolute Deviation (MAD)11
Skewness-0.03297336068
Sum968553
Variance142.3935424
MonotocityNot monotonic
2020-12-12T20:59:20.706285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%) 
1944469.6%
 
3642309.1%
 
636097.8%
 
2129726.4%
 
3427996.0%
 
2721164.6%
 
1720374.4%
 
420254.4%
 
4117123.7%
 
2616163.5%
 
1015423.3%
 
2313392.9%
 
3213392.9%
 
113382.9%
 
3512952.8%
 
312692.7%
 
119852.1%
 
99782.1%
 
128231.8%
 
167411.6%
 
27391.6%
 
406331.4%
 
226251.3%
 
295971.3%
 
315891.3%
 
Other values (16)39668.5%
 
ValueCountFrequency (%) 
113382.9%
 
27391.6%
 
312692.7%
 
420254.4%
 
55161.1%
 
636097.8%
 
71830.4%
 
8390.1%
 
99782.1%
 
1015423.3%
 
ValueCountFrequency (%) 
4117123.7%
 
406331.4%
 
392040.4%
 
384150.9%
 
37810.2%
 
3642309.1%
 
3512952.8%
 
3427996.0%
 
33470.1%
 
3213392.9%
 

Police Districts
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing119
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.11200742
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size363.3 KiB
2020-12-12T20:59:20.786354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.125143008
Coefficient of variation (CV)0.6113338169
Kurtosis-1.424090765
Mean5.11200742
Median Absolute Deviation (MAD)3
Skewness0.09498061097
Sum237008
Variance9.766518819
MonotocityNot monotonic
2020-12-12T20:59:20.845405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1878218.9%
 
9803517.3%
 
2535511.5%
 
4513611.0%
 
642019.0%
 
535907.7%
 
1031926.9%
 
829376.3%
 
726705.7%
 
324655.3%
 
(Missing)1190.3%
 
ValueCountFrequency (%) 
1878218.9%
 
2535511.5%
 
324655.3%
 
4513611.0%
 
535907.7%
 
642019.0%
 
726705.7%
 
829376.3%
 
9803517.3%
 
1031926.9%
 
ValueCountFrequency (%) 
1031926.9%
 
9803517.3%
 
829376.3%
 
726705.7%
 
642019.0%
 
535907.7%
 
4513611.0%
 
324655.3%
 
2535511.5%
 
1878218.9%
 

Interactions

2020-12-12T20:59:02.817892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:02.901464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:02.985035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.067606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.146174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.231247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.309815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.390384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.470953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.559030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.641601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.724672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.801738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.883308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:03.965879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T20:59:04.126017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:04.210090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:04.288657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T20:59:04.449796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T20:59:04.866655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:04.947724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:05.029294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:05.105861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:05.189933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T20:59:05.347568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T20:59:05.753418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:05.829983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:05.906549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:05.982615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.054177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.133245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.204806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.280872image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.354435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.438007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.518076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.601147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.674711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:06.762786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T20:59:06.934935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.017005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.104080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.185650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.270223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.354796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.443372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.529947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.619524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.702095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.778160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.855226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:07.931292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.004355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.081922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.153483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.227547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.298608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.377176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.453741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.534311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.607374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.688944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.771015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.852084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:08.927649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.011221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.087787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.166355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.243421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.326993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.406561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.490133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.566199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.643265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.719331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.795396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.866958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:09.945025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.017087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.092151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.164213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.244282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.319847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.398915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.472479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.561055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.648130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.735205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.816274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.903850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:10.985420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.069993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.152063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.241140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.326213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.412788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.495359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.575928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.657498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.736566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.812632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.895703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:11.971268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.051337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.127403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.210474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.290043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.374616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.451682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.539757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.632337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.716910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.796479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.882553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:12.962621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.047194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.127764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.214839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.297410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.382983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.465054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.542120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.620688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.699255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.770317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.847883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.918945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:13.992508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:14.065070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:14.143638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:14.218202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:14.295268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T20:59:20.917967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T20:59:21.079106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T20:59:21.240745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T20:59:21.411892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T20:59:21.593048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T20:59:14.676096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:15.287622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:15.742514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T20:59:16.052781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Complaint IdPrimaryComplaint NumberComplaint Item TypeComplaint Item Type Description311 Case NumberInspection NumberAddressZipcodeBattalionStation AreaFire Prevention DistrictReceived DateEntry DateDispositionDisposition DateSupervisor DistrictNeighborhood DistrictLocationNeighborhoods_from_fyvs_ahh9Supervisor DistrictsFire Prevention DistrictsCurrent Police DistrictsNeighborhoods - Analysis BoundariesZip CodesNeighborhoods (old)Police Districts
02004-017398YY2004-017398multiple fire code violationsNaNNaN201 Harrison St94105.0033503S04/28/202004/28/2020violation issued04/28/20206.0Financial District/South BeachPOINT (-122.39061102000001 37.78767972002966)6.09.06.02.08.028855.06.02.0
10504-000299YY0504-000299uncategorized complaintNaNNaN145 Natoma St94105.0030103N04/05/200504/05/2005violation issued04/05/20056.0Financial District/South BeachPOINT (-122.39983309 37.78615756002967)6.09.012.02.08.028855.06.02.0
20504-000399YY0504-000399uncategorized complaintNaNNaN145 Natoma St94105.0030103N04/05/200504/05/2005violation issued04/05/20056.0Financial District/South BeachPOINT (-122.39983309 37.78615756002967)6.09.012.02.08.028855.06.02.0
30504-000505YY0504-000505alarm systemsNaN9252.01350 California St94109.0014101W04/22/200506/10/2005condition corrected07/20/20053.0Nob HillPOINT (-122.41669573 37.79138597002966)21.010.05.01.021.028858.021.01.0
40504-000602YY0504-000602blocked exitsNaN8354.0145 Natoma St94105.0030103N04/25/200504/25/2005NaN05/03/20056.0Financial District/South BeachPOINT (-122.39983309 37.78615756002967)6.09.012.02.08.028855.06.02.0
50504-000799YY0504-000799uncategorized complaintNaNNaN375 Woodside Ave94131.008200804/26/200504/26/2005violation issued04/26/20057.0Twin PeaksPOINT (-122.45252787000001 37.745952380029635)38.04.01.08.038.059.038.05.0
61909-026005YY1909-026005alarm systemsNaN396742.0935 Buena Vista Ave West94117.005210509/26/201909/26/2019violation issued09/27/20195.0Haight AshburyPOINT (-122.44339396 37.77002504002966)9.011.015.08.03.029492.09.05.0
71909-012005YY1909-012005alarm systemsNaN396155.0275 Grattan St94117.005120509/13/201909/16/2019violation issued09/27/20195.0Haight AshburyPOINT (-122.45206557999998 37.763594690029656)9.011.015.08.03.029492.09.05.0
81909-027498YY1909-027498multiple fire code violationsNaNNaN350 Girard St94134.010421009/27/201909/27/2019violation issued09/27/20199.0PortolaPOINT (-122.40548680000002 37.7277717800296)28.07.010.03.025.0309.028.03.0
91909-027098YY1909-027098multiple fire code violationsNaN371990.034 Buchanan St94102.0023602N09/27/201909/27/2019violation issued09/27/20198.0Hayes ValleyPOINT (-122.42635737999998 37.77015932002966)10.05.07.06.09.028852.010.09.0

Last rows

Complaint IdPrimaryComplaint NumberComplaint Item TypeComplaint Item Type Description311 Case NumberInspection NumberAddressZipcodeBattalionStation AreaFire Prevention DistrictReceived DateEntry DateDispositionDisposition DateSupervisor DistrictNeighborhood DistrictLocationNeighborhoods_from_fyvs_ahh9Supervisor DistrictsFire Prevention DistrictsCurrent Police DistrictsNeighborhoods - Analysis BoundariesZip CodesNeighborhoods (old)Police Districts
464721905-034205YY1905-03425alarm systemsNaN382520.02130 Jackson St94115.004380405/31/201905/31/2019condition corrected06/03/20192.0Pacific HeightsPOINT (-122.43041278000001 37.79327111002967)27.01.013.06.030.029490.027.09.0
464731905-034305YY1905-03435alarm systemsNaN382521.02150 Jackson St94115.004380405/31/201905/31/2019condition corrected06/03/20192.0Pacific HeightsPOINT (-122.43063491000001 37.79324291002967)27.01.013.06.030.029490.027.09.0
464741905-034420YY1905-034420electrical systemsNaN382522.0135 Capp St94110.0020702S05/31/201905/31/2019condition corrected06/10/20199.0MissionPOINT (-122.41835847 37.76454722002965)19.07.08.07.020.028859.019.04.0
464751905-034006YY1905-03406extinguishersNaN382517.01081 Le Conte Ave94124.010441005/31/201905/31/2019condition corrected06/04/201910.0Bayview Hunters PointPOINT (-122.39838351 37.71910355002961)1.08.010.03.01.058.01.03.0
464761905-034019NaN1905-034019sprinkler/standpipe systemsNaN382517.01081 Le Conte Ave94124.010441005/31/201905/31/2019violation issued06/12/201910.0Bayview Hunters PointPOINT (-122.39838351 37.71910355002961)1.08.010.03.01.058.01.03.0
464771905-034105YY1905-03415alarm systemsNaN382518.0366 - 390 Golden Gate Ave94102.0020302N05/30/201905/31/2019no merit06/21/20196.0TenderloinPOINT (-122.41685074 37.781768140029655)36.09.07.010.036.028852.036.010.0
464781905-033805YY1905-03385alarm systemsNaN382512.03868 - 3876 Noriega St94122.008230805/30/201905/31/2019condition corrected07/18/20194.0Sunset/ParksidePOINT (-122.50501206 37.753204670029646)35.03.01.05.035.056.035.08.0
464791905-034523YY1905-034523exit maintenanceNaN382544.0405 - 465 Davis Ct94111.0011301N05/30/201905/31/2019no merit08/07/20193.0Financial District/South BeachPOINT (-122.39840845 37.796397720029674)6.010.03.01.08.028860.06.01.0
464801905-033905YY1905-03395alarm systemsNaN382514.0318 - 322 Kearny St94104.0011301S05/30/201905/31/2019violation issued06/04/20193.0Financial District/South BeachPOINT (-122.40388761999999 37.791169730029644)6.010.04.01.08.028854.06.01.0
464811905-034698YY1905-034698multiple fire code violationsNaNNaN188 - 190 King St94107.0030803S05/31/201905/31/2019violation issued05/31/20196.0Mission BayPOINT (-122.39169561000001 37.77852716002965)20.09.06.02.04.028856.020.02.0